Conventional Synthetic Aperture Radar Research Articles

Performance Evaluation of Spaceborne Bistatic SAR Systems

In this paper, the performances of spaceborne bistatic synthetic aperture radar (SAR) systems are evaluated and compared with those of the conventional monostatic SAR system. Drawing on information on the altitude of satellites and the pulse repetition frequency of the system, the distance between the satellites carrying the transmitter and the receiver is first determined to then obtain the maximum observable width by overlapping the blind ranges. The performances of the bistatic SAR for two satellites at the same altitude moving parallel to each other at the same speed are evaluated, and the results are compared with those obtained from a monostatic SAR system located at left or right end of the baseline. The resolutions, noise equivalent sigma zero, and ambiguities of the bistatic SAR are evaluated based on the windows between blind incident angles of a timing diagram. Subsequently, the most ap-propriate window is determined by comparing the evaluated performances of the bistatic SAR.

A Fast Total-Variation Driven Network for Sparse Aperture ISAR Imaging

Conventional Fourier transform-based Inverse Synthetic Aperture Radar (ISAR) imaging cannot deal with the cases of incomplete radar echoes, in which the data needs to be specially processed by Sparse Aperture (SA) imaging methods. Most current SA-ISAR imaging employs sparsity-driven optimization methods. However, the assumption that the target reflectivity is sparse in the spatial domain is insufficient to capture the surface and edge features of the target if no sparse representations are found for the target and well incorporated in the imaging process. In view of the edge-preserving capability of Total Variation (TV) and the strong learning ability of the Deep Neural Network (DNN), we propose a TV-driven network to improve the SA ISAR imaging performance. We first develop a fast TV regularization method to perform the imaging where the Gradient Descent (GD) along with the Momentum Acceleration (MA) are incorporated to increase the computational efficiency. Then, we unwrap the iterative fast TV regularization into a cascaded neural network to make the key imaging parameters learnable, leading to improved imaging performance, which we refer to as FGDTV-Net. Experiments under a variety of scenarios show that the proposed FGDTV-Net for SA-ISAR imaging is superior to existing SA imaging algorithms in preserving surface and edge features and is more robust in low signal-to-noise scenarios.

Enhancing Road Extraction in Large-Scale Complex Terrain through Multi-Source Remote Sensing Image Fusion and Optimization

In large-scale remote sensing scenarios characterized by intricate terrain, the straightforward road imaging features in synthetic aperture radar (SAR) images make them susceptible to interference from other elements such as ridges, compromising the robustness of conventional SAR image road extraction methods. This paper introduces a method that integrates Gaofen-3 (GF-3) with a resolution of 3.0 m, Digital Elevation Models (DEMs), and Gaofen-2 (GF-2) remote sensing image data with a resolution of 4.0 m, aiming to improve the performance of road extraction in complex terrain. Leveraging DEMs, this study addresses the limitations in feature-based SAR algorithms, extending their application to complex remote sensing scenarios. Decision-level fusion, integrating SAR and multispectral images, further refines road extraction precision. To overcome issues related to terrain interference, including fragmented road segments, an adaptive rotated median filter and graph-theory-based optimization are introduced. These advancements collectively enhance road recognition accuracy and topological precision. The experimental results validate the effectiveness of the multi-source remote sensing image fusion and optimization methods. Compared to road extraction from multispectral images, the F1-score of the proposed method on the test images increased by 2.18%, 4.22%, and 1.4%, respectively.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

Ground subsidence associated with mining activity in the Ningdong coal base area, northwestern China revealed by InSAR time series analysis

Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

Terahertz 3D point cloud imaging for complex targets.

The reconstruction of complex targets using terahertz technology is often hindered by diffraction and interference of electromagnetic waves, leading to the loss of fine target details. In this research article, we have introduced a terahertz synthetic aperture radar (SAR) imaging method that integrates an iterative closest point (ICP) algorithm, referred to as SAR-ICP, to achieve accurate reconstruction of intricate target structures. To accomplish this, multiple sets of point cloud data are acquired by varying the illumination viewpoint. The ICP algorithm is then employed to align and fuse these datasets, resulting in the generation of high-quality three-dimensional (3D) images. The experimental results validate the effectiveness of the proposed SAR-ICP method. The information entropy of the reconstructed 3D image using the SAR-ICP is approximately 0.05 times that of the conventional SAR method, indicating a superior image quality. In the future, we anticipate the widespread application of this method in areas such as security inspection, non-destructive testing, and other complex scenarios.

Enhanced Forward-Looking Missile-Borne Bistatic SAR Imaging With Electromagnetic Vortex

Bistatic radar possesses the forward-looking imaging performance that conventional synthetic aperture radar (SAR) lacks. Nevertheless, bistatic forward-looking imaging is seriously limited by the motion of the transmitter and receiver, resulting in a restrained resolution. In this work, the method for vortex missile-borne bistatic SAR (BSAR) is proposed, which combines the orbital angular momentum (OAM) beam with the traditional missile-borne BSAR technique to obtain a larger Doppler bandwidth. Higher azimuth resolution is achieved by compensating large spatial range cell migration (RCM) and serious range-azimuth coupling. First, the imaging scene of vortex missile-borne BSAR and the OAM-based echo model are established and deduced. Second, the preprocessing method for amplitude compensation is proposed with the design of the transmitted OAM beam, which improves the uncorrelation between different pulse echoes. Then, the improved range Doppler (RD) algorithm with azimuth compensation factor is proposed for vortex missile-borne BSAR to obtain the super-resolution 2-D reconstruction for the target. Simulation results show that compared with conventional missile-borne BSAR, the proposed vortex missile-borne BSAR possessing the extending Doppler bandwidth can obtain superior azimuth resolution performance with the identical synthetic aperture length situation. Furthermore, this work will contribute to the application for OAM-carrying in the field of radar forward-looking imaging and the development for high-resolution missile-borne BSAR imaging technology.

UAV-Based Bistatic SAR-Imaging Using a Stationary Repeater

Synthetic aperture radar (SAR) is an established technique for high-resolution radar imaging relying on the ego-motion of the radar. The long coherent integration time of the radar in conjunction with a linear or circular trajectory enables the illumination of targets under different observation angles leading to robustness against short-time interference signals. The use of a bistatic setup to simultaneously acquire multiple observation angles further increases the aforementioned benefits. In this work, a bistatic SAR system using an active repeater is proposed, in the following called Bistatic Repeater-SAR (BR-SAR). In BR-SAR the repeater acts as a second observer providing a bistatic path with low system cost and hardware effort. This article discusses the BR-SAR approach with all signal processing and hardware considerations necessary to build the bistatic radar system. This includes a description of the full system and signal chain with a focus on the signal separation and evaluation of both monostatic and bistatic signals and the subsequent SAR processing. The approach is verified via simulations and measurements, in which conventional SAR is compared to the proposed BR-SAR system. Hardware effects leading to a deteriorated system performance of the bistatic system are analyzed and countermeasures are discussed and implemented. Finally, the benefits of the bistatic system are demonstrated based on a realistic measurement scenario.

Cross-task and cross-domain SAR target recognition: A meta-transfer learning approach

Meta learning and transfer learning offer promising solutions to the problem of requiring large amounts of data in deep learning approaches for synthetic aperture radar (SAR) target recognition. To improve their performance further, we propose a novel Meta-transfer learning approach for cross-task and cross-domain SAR target recognition (MetraSAR). In the meta training phase, we train a robust meta learner with the human-like ability to master new knowledge quickly across tasks and domains. By designing the weighted classification loss with class weights, we conduct hard class mining that forces the meta learner to grow stronger. In addition to the external knowledge transfer across different tasks, we achieve the internal transfer across domains by using the domain confusion loss with a domain discriminator. To balance the two designed loss terms, we adopt the multi-gradient descent algorithm to optimize the meta learner adaptively. In the meta testing phase, the trained robust meta learner is transferred to solve the new task with few shot samples and a quick generalization. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset validate that MetraSAR has better performance than conventional SAR target recognition methods.

Implementation Method of Automotive Video SAR (ViSAR) Based on Sub-Aperture Spectrum Fusion

The automotive synthetic aperture radar (SAR) can obtain two-dimensional (2-D) high-resolution images and has good robustness compared with the other sensors. Generally, the 2-D high-resolution always conflicts with the real-time requirement in conventional SAR imaging. This article suggests an automotive video SAR (ViSAR) imaging technique based on sub-aperture spectrum fusion to address this issue. Firstly, the scene space variation problem caused by close observation distance in automotive SAR is analyzed. Moreover, the sub-aperture implementation method, frame rate and resolution of automotive ViSAR are also introduced. Then, the improved Range Doppler algorithm (RDA) is used to focus the sub-aperture data. Finally, a sub-aperture stitching strategy is proposed to obtain a high-resolution frame image. Compared with the available ViSAR imaging method, the proposed method is more efficient, performs better, and is more appropriate for automotive ViSAR. The simulation results and actual data of the automotive SAR validate the effectiveness of the proposed method.

Designing of Synthetic Aperture Radar Based Control Algorithms for the Autonomous Vehicles

Abstract— The rise in popularity of self-driving cars can be attributed to advancements in modern technology. The surge in interest in self-driving cars has led to an increase in their development, but this has also brought some challenges. A large part of the solution to these problems is satellite remote sensing and GIS technology. Optical data remote sensing technologies alone have limited potential for long-term forest management sustainability. Active Synthetic Aperture Radar (SAR) remote sensing technology has grown in importance in forestry because of its uniqueness and rapid advancement. For example, SAR has an all-weather capability that is sun light independent, cloud and rain-resistant, and highly penetrating. SAR and optical, SAR and LiDAR, optical and LiDAR remote sensing have all been shown to be useful for accurate forest AGB estimation when compared to single sensor data. These types of sensor data integrations are becoming increasingly common. This is made possible by the fact that the scattering process heavily influences the polarimetric signatures that can be observed. The inclusion of SAR polarimetry improves classification and segmentation quality compared to conventional SAR with a single channel. Decomposition products' outputs have been classified. Keywords— Synthetic Aperture Radar, LiDAR, Autonomous Vehicles,

A Novel Method for Staggered SAR Imaging in an Elevation Multichannel System

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

Retrieval of 2-D Ocean Total Surface Current From Sentinel-1 Doppler Frequency Shift Using 2-D Kalman Filtering

Synthetic aperture radar (SAR) has all-weather, all-day, large-area observation capability and can measure ocean total surface currents with Doppler frequency shift. However, conventional SAR can only obtain the radial velocity of the ocean total surface current. In this letter, we propose a method for inverting ocean total surface current vectors using a two-dimensional (2D) Kalman filter and a priori information about ocean current fields from Ocean Surface Current Analyses Real-time (OSCAR). We establish a complete filtering process including filter creation, filtering path selection and fusion of filtering results. We construct the Kalman filter according to the motion characteristics of ocean currents and transform the ocean surface current vector inversion problem into a filtering problem. The introduction of two-dimensional filtering can better adapt to the directionality of ocean currents. Furthermore, we design the filtering path to obtain smoother filtering results. In order to synthesize the filtering results of different filtering paths, we use the Covariance Intersection (CI) fusion method in distributed fusion to obtain the final ocean current vector inversion results. The accuracy of the inversion is verified by the measured data. The Root Mean Square Error (RMSE) of the proposed method with the mean Doppler error of 5.43Hz is 0.182 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m/s</i> in eastward speed and 0.141 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m/s</i> in northward speed. The reduction is 0.009 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m/s</i> and 0.029 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m/s</i> against OSCAR.

SAR Imaging in Frequency Scan Mode: System Optimization and Potentials for Data Volume Reduction

Frequency scanning (FScan) is an innovative acquisition mode for synthetic aperture radar (SAR) systems. The method is based on the frequency-dependent beam pointing capabilities of phased array antennas, artificially increased via the combined use of true time delays and phase shifters within the array antenna. By this, typical limitations of conventional SAR systems in terms of achievable swath width and azimuth resolution can be mitigated, and so a wide swath can be imaged maintaining a fine azimuthal resolution. In the first part of the article, we introduce the theoretical concept, which is necessary to evaluate the reduced echo window length (EWL) with respect to equivalent stripmap data and the implications for the transmit pulse characterization. An FScan sensor flying in a TerraSAR-X-like orbit is shown to be capable of imaging an 80-km wide swath with 1-m azimuth resolution. The resulting time–frequency properties of the recorded raw data make the traditional SAR data compression algorithms such as block-adaptive quantization (BAQ) highly inefficient in this case. Therefore, the second part of the article investigates dedicated quantization methods for efficient data volume reduction in FScan systems. Different solutions are investigated and evaluated through simulations. Various transformations of the raw data have been exploited to optimize the encoding process, including deramping, fast Fourier transform (FFT), and blockwise approaches. Compared with standard BAQ in the time domain, the suggested data compression methods significantly improve the resulting signal-to-quantization noise ratio, allowing for the reduction in the overall data volume by about 60% for the considered system scenario, while maintaining robustness in the presence of inhomogeneous scene characteristics at the cost of a modest complexity increase for its on-board implementation.

Bibliometric Analysis of the Permafrost Research: Developments, Impacts, and Trends

Permafrost is a significant part of the cryosphere, which has gained increasing attention from scientists, policy-makers, and the general public due to global warming, environmental degradation, water shortages, and intense human activities. Although many permafrost research review articles have been published, these studies were predominantly limited to either one subject or one field, while systematic studies about permafrost based on bibliometric analysis methods remain limited. We aim to fill this gap by conducting a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database. The results indicate that permafrost research is a typically multi-author, multi-country, and multi-institution cooperative field, involved in many research fields. The cumulative number of publications has presented an exponential increase over the past 80 years, with an average annual growth rate of 10.40%. Since 2000, China has seen a rapid growth in the number of publications per year, surpassing the USA in 2016 and leading in the years since then. In addition, the authors from China have great contributions in publications, and there is good room for permafrost development in the future according to the authors’ M-index ranking. After the analysis of authors’ keywords, we found that, compared to the conventional methods, machine learning and interferometric synthetic aperture radar (InSAR) are new technological approaches introduced in recent years, and the Qinghai–Tibet Plateau has become a popular study area. The results presented here can help related researchers, scholars, and students in the field to better understand the past developments, current status, and future trends of permafrost research. Furthermore, this paper presents and expands the general process of the bibliometric method used in permafrost studies, which can provide researchers with new inspirations and improve discipline research approach.

Deformation monitoring and influence factor analysis of expressway over strong saline soil based on an advanced multi-temporal InSAR technique

Saline soil harbors crystallized salt in the solid phase and salt solution in the liquid phase. By the natural environmental and human activity factors, migration and accumulation of the salt and crystallization process alternated with dissolving, which makes saline soil with significant seasonal differences appear in the deformation and regional scattering characteristic. These phenomena raise the limitation of conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and consequently high-precision deformation monitoring of strong saline soil subgrade and pavement. To overcome the limitations, this study aimed to propose an advanced MT-InSAR method, which considers the seasonal interferometric coherences caused by precipitation and the temporal physical deformation evolution of the subgrade and pavement over strong saline soil. To present the better performance of the advanced method, a segment of the Qarhan–Golmud Expressway (QGE), which is the first expressway built in the strong saline soil area in China, was selected for this study. Two sets of SAR images acquired from January 2018 to January 2022 from Sentinel-1A ascending and descending orbits over the QGE were utilized, 774 and 928 seasonal filtered interferograms are optimized for deformation monitoring based on the deformation Poisson curve (PC) model. Compared with the method in the previous studies, the accuracy, efficiency, and reliability of the monitoring results increased dramatically. Subsequently, further discussions are conducted in detail on the regularity of expressway deformation over strong saline soil, especially from physical and chemical perspectives. Findings show that the ratio between Cl− and SO42- determines the deformation distribution and morphology. Also, the precipitation and temperature affect the seasonal characteristics. The contributions of this investigation might provide technical references for related expressway management and policy-making departments to ensure the long-term safe operation and stability of the QGE.

Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing.

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images.

A Novel SAR Imaging Method Based on Morphological Component Analysis

Clutter suppression plays an important role in a synthetic aperture radar (SAR) system. The conventional SAR imaging methods are useful for distinguishing the echo signal and noise, but cannot separate the target signal from background clutter. Inspired by the signal separation ability of morphological component analysis (MCA), a novel SAR imaging method based on MCA is proposed to suppress the strong background clutter. In the new model, the SAR echo is considered as a linear superposition of target signal, clutter signal, and noise. According to different characteristics of morphological components, clutter dictionary and target dictionary are constructed to sparsely represent the clutter component and target component, respectively. Then, the MCA method based on the sparse representation and morphological diversity of signals is employed to decompose the SAR echo into the target signal, clutter signal, and noise. Finally, the separated target signal is processed to obtain the ultimate SAR image. Experimental results from simulated and real SAR data are provided to demonstrate the effectiveness of the proposed method.

Using Range Split-Spectrum Interferometry to Reduce Phase Unwrapping Errors for InSAR-Derived DEM in Large Gradient Region

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%.

Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super-resolution imaging, multi-target resolution, and cognitive reconfiguration. This paper proposes a fast, super-resolution imaging method employing continuous compressive sensing for sparse-aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low-rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super-resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super-resolution image and shows better performance than state-of-the-art methods.